AutoTabular

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models tabular data.

What's good in it?

• It is using the RAPIDS as back-end support, gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs.
• It Supports many anomaly detection models: ,
• It using meta learning to accelerate model selection and parameter tuning.
• It is using many Deep Learning models for tabular data: Wide&Deep, DCN(Deep & Cross Network), FM, DeepFM, PNN ...
• It is using many machine learning algorithms: Baseline, Linear, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, and Nearest Neighbors.
• It can compute Ensemble based on greedy algorithm from Caruana paper.
• It can stack models to build level 2 ensemble (available in Compete mode or after setting stack_models parameter).
• It can do features preprocessing, like: missing values imputation and converting categoricals. What is more, it can also handle target values preprocessing.
• It can do advanced features engineering, like: Golden Features, Features Selection, Text and Time Transformations.
• It can tune hyper-parameters with not-so-random-search algorithm (random-search over defined set of values) and hill climbing to fine-tune final models.

Example

First, install dependencies

# clone project
git clone https://apulis-gitlab.apulis.cn/apulis/AutoTabular/autotabular.git

# install project
cd autotabular
pip install -e .
pip install -r requirements.txt

Next, navigate to any file and run it.

# module folder
cd example

# run module (example: mnist as your main contribution)
python demo.py

Citation

If you use AutoTabular in a scientific publication, please cite the following paper:

Robin, et al. "AutoTabular: Robust and Accurate AutoML for Structured Data." arXiv preprint arXiv:2003.06505 (2021).

BibTeX entry:

@article{agtabular,
  title={AutoTabular: Robust and Accurate AutoML for Structured Data},
  author={JianZheng, WenQi},
  journal={arXiv preprint arXiv:2003.06505},
  year={2021}
}

License

This library is licensed under the Apache 2.0 License.

Contributing to AutoTabular

We are actively accepting code contributions to the AutoTabular project. If you are interested in contributing to AutoTabular, please contact me.